| from __future__ import annotations
|
| import os
|
| from collections import namedtuple
|
| import enum
|
|
|
| from modules import sd_models, cache, errors, hashes, shared
|
|
|
| NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
|
|
|
| metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
|
|
|
|
|
| class SdVersion(enum.Enum):
|
| Unknown = 1
|
| SD1 = 2
|
| SD2 = 3
|
| SDXL = 4
|
|
|
|
|
| class NetworkOnDisk:
|
| def __init__(self, name, filename):
|
| self.name = name
|
| self.filename = filename
|
| self.metadata = {}
|
| self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
|
|
|
| def read_metadata():
|
| metadata = sd_models.read_metadata_from_safetensors(filename)
|
| metadata.pop('ssmd_cover_images', None)
|
|
|
| return metadata
|
|
|
| if self.is_safetensors:
|
| try:
|
| self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
|
| except Exception as e:
|
| errors.display(e, f"reading lora {filename}")
|
|
|
| if self.metadata:
|
| m = {}
|
| for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
|
| m[k] = v
|
|
|
| self.metadata = m
|
|
|
| self.alias = self.metadata.get('ss_output_name', self.name)
|
|
|
| self.hash = None
|
| self.shorthash = None
|
| self.set_hash(
|
| self.metadata.get('sshs_model_hash') or
|
| hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
|
| ''
|
| )
|
|
|
| self.sd_version = self.detect_version()
|
|
|
| def detect_version(self):
|
| if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
|
| return SdVersion.SDXL
|
| elif str(self.metadata.get('ss_v2', "")) == "True":
|
| return SdVersion.SD2
|
| elif len(self.metadata):
|
| return SdVersion.SD1
|
|
|
| return SdVersion.Unknown
|
|
|
| def set_hash(self, v):
|
| self.hash = v
|
| self.shorthash = self.hash[0:12]
|
|
|
| if self.shorthash:
|
| import networks
|
| networks.available_network_hash_lookup[self.shorthash] = self
|
|
|
| def read_hash(self):
|
| if not self.hash:
|
| self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
|
|
|
| def get_alias(self):
|
| import networks
|
| if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
|
| return self.name
|
| else:
|
| return self.alias
|
|
|
|
|
| class Network:
|
| def __init__(self, name, network_on_disk: NetworkOnDisk):
|
| self.name = name
|
| self.network_on_disk = network_on_disk
|
| self.te_multiplier = 1.0
|
| self.unet_multiplier = 1.0
|
| self.dyn_dim = None
|
| self.modules = {}
|
| self.bundle_embeddings = {}
|
| self.mtime = None
|
|
|
| self.mentioned_name = None
|
| """the text that was used to add the network to prompt - can be either name or an alias"""
|
|
|
|
|
| class ModuleType:
|
| def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
|
| return None
|
|
|
|
|
| class NetworkModule:
|
| def __init__(self, net: Network, weights: NetworkWeights):
|
| self.network = net
|
| self.network_key = weights.network_key
|
| self.sd_key = weights.sd_key
|
| self.sd_module = weights.sd_module
|
|
|
| if hasattr(self.sd_module, 'weight'):
|
| self.shape = self.sd_module.weight.shape
|
|
|
| self.dim = None
|
| self.bias = weights.w.get("bias")
|
| self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
|
| self.scale = weights.w["scale"].item() if "scale" in weights.w else None
|
|
|
| def multiplier(self):
|
| if 'transformer' in self.sd_key[:20]:
|
| return self.network.te_multiplier
|
| else:
|
| return self.network.unet_multiplier
|
|
|
| def calc_scale(self):
|
| if self.scale is not None:
|
| return self.scale
|
| if self.dim is not None and self.alpha is not None:
|
| return self.alpha / self.dim
|
|
|
| return 1.0
|
|
|
| def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
|
| if self.bias is not None:
|
| updown = updown.reshape(self.bias.shape)
|
| updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
|
| updown = updown.reshape(output_shape)
|
|
|
| if len(output_shape) == 4:
|
| updown = updown.reshape(output_shape)
|
|
|
| if orig_weight.size().numel() == updown.size().numel():
|
| updown = updown.reshape(orig_weight.shape)
|
|
|
| if ex_bias is not None:
|
| ex_bias = ex_bias * self.multiplier()
|
|
|
| return updown * self.calc_scale() * self.multiplier(), ex_bias
|
|
|
| def calc_updown(self, target):
|
| raise NotImplementedError()
|
|
|
| def forward(self, x, y):
|
| raise NotImplementedError()
|
|
|
|
|
